While XiaoIce blends multiple functionalities, the design focuses on creating an engaging conversation. This is predicated on the philosophy that the primary reason we have a conversation is because it is enjoyable rather than to get something out of it. This is in contrast to the bots that simply focus on task completion where the goal is typically to get the task completed with as few conversational turns as possible. The main metric for measuring long-term user engagement in XiaoIce is conversational-turns per session (CPS). That is, a longer engaging conversation will be considered typically better in XiaoIce. This is similar to a viewpoint taken in other settings such as the Alexa Challenge [1]} which considered conversation length as a signal for positive engagement while also considering other factors such as coherence, topic diversity, etc. In order to prevent simple methods of extending a conversation that fail to provide much depth (e.g. like the ELIZA system above), XiaoIce also considers the number of active users (NAU) to compute the expected CPS. This is analogous to information systems which consider sessions per unique user as a sign of long-term engagement while the short-term engagement signal is the number of turns per session. As reported in [2]} XiaoIce ultimately reached an average of 23 CPS across – one of the highest reported lengths of engagement for chatbots.
